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Infrared Nanoprobes and TissuE mimickinG tumoR shApe identificaTION

Project description

Precision imaging for cancer surgery

Where do tumour cells end and normal cells start? This is not an easy question to answer, despite all the advanced technology that is available today. Supported by the Marie Sklodowska-Curie Actions programme, the INTEGRATION project aims to change this through the development of a state-of-the-art platform that integrates near-infrared nanoprobes, 3D bioprinting, and machine learning. By operating in the underexplored NIR-III spectrum, the project aims to deliver unprecedented image contrast in deep tissue. The project’s goal is to develop brain-like systems using 3D bioprinting methods and train algorithms to define tumour edges with extreme precision. This could contribute to enhancing accuracy of surgery, reducing the chances of cancer recurrence, and reducing animal experimentation.

Objective

Cancer surgery often comes down to a crucial question: where does the tumor end, and healthy tissue begin? One wrong cut can leave malignant cells behind or damage vital functions. INTEGRATION addresses this challenge by combining three cutting-edge approaches into a single preclinical platform: near-infrared-emitting nanoprobes, 3D-bioprinted brain-like models, and machine learning algorithms. By operating in the underexplored third biological transparency window (NIR-III, 1550-1850 nm), where tissue scattering is reduced and autofluorescence disappears, INTEGRATION will deliver sharper, higher-contrast images of tumor boundaries than currently possible.
To validate this strategy, custom fluorescent nanoprobes will be engineered for stability, biocompatibility, and strong NIR-III emission. These will be tested in lifelike tissue phantoms created with 3D bioprinting, where healthy and tumoral regions are reproduced with tunable shape and optical properties. Fluorescence images collected from these models will train a convolutional neural network (U-Net) to precisely segment tumor margins in three dimensions. Finally, the approach will be validated ex vivo in mouse brain tissues, generating a proof-of-concept pipeline that is rigorous, ethical, and sustainable.
Expected outcomes include the development of reliable NIR-III contrast agents, protocols for brain-mimicking phantom fabrication, and a machine-learning-based method for accurate tumor segmentation. INTEGRATION thus paves the way for safer surgeries, fewer relapses, and faster translation toward real-time fluorescence image-guided operations. Beyond healthcare, it reduces dependence on animal models, strengthens Europe’s leadership in nanomaterials, bioprinting, and machine learning, and supports the long-term vision of precision medicine and robotic-assisted surgery.

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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(opens in new window) HORIZON-MSCA-2025-PF

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Coordinator

UNIVERSIDAD AUTONOMA DE MADRID
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 209 914,56
Address
CALLE EINSTEIN 3 CIUDAD UNIV CANTOBLANCO RECTORADO
28049 MADRID
Spain

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Region
Comunidad de Madrid Comunidad de Madrid Madrid
Activity type
Higher or Secondary Education Establishments
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Total cost

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